Functionally distinct regions of the locus Leishmania major response 15 control IgE or IFNγ level in addition to skin lesions.

Leishmaniasis, a disease caused by parasites of Leishmania spp., endangers more than 1 billion people living in endemic countries and has three clinical forms: cutaneous, mucocutaneous, and visceral. Understanding of individual differences in susceptibility to infection and heterogeneity of its pathology is largely lacking. Different mouse strains show a broad and heterogeneous range of disease manifestations such as skin lesions, splenomegaly, hepatomegaly, and increased serum levels of immunoglobulin E and several cytokines. Genome-wide mapping of these strain differences detected more than 30 quantitative trait loci (QTLs) that control the response to Leishmania major. Some control different combinations of disease manifestations, but the nature of this heterogeneity is not yet clear. In this study, we analyzed the L. major response locus Lmr15 originally mapped in the strain CcS-9 which carries 12.5% of the genome of the resistant strain STS on the genetic background of the susceptible strain BALB/c. For this analysis, we used the advanced intercross line K3FV between the strains BALB/c and STS. We confirmed the previously detected loci Lmr15, Lmr18, Lmr24, and Lmr27 and performed genetic dissection of the effects of Lmr15 on chromosome 11. We prepared the interval-specific recombinant strains 6232HS1 and 6229FUD, carrying two STS-derived segments comprising the peak linkage of Lmr15 whose lengths were 6.32 and 17.4 Mbp, respectively, and analyzed their response to L. major infection. These experiments revealed at least two linked but functionally distinct chromosomal regions controlling IFNγ response and IgE response, respectively, in addition to the control of skin lesions. Bioinformatics and expression analysis identified the potential candidate gene Top3a. This finding further clarifies the genetic organization of factors relevant to understanding the differences in the individual risk of disease.

Structure vs. chemistry: Alternate mechanisms for controlling leaf microbiomes.

The analysis of phyllosphere microbiomes traditionally relied on DNA extracted from whole leaves. To investigate the microbial communities on the adaxial (upper) and abaxial (lower) leaf surfaces, swabs were collected from both surfaces of two garden plants, Rhapis excelsa and Cordyline fruticosa. Samples were collected at noon and midnight and at five different locations to investigate if the phyllosphere microbial communities change with time and location. The abaxial surface of Rhapis excelsa and Cordyline fruticosa had fewer bacteria in contrast to its adaxial counterpart. This observation was consistent between noon and midnight and across five different locations. Our co-occurrence network analysis further showed that bacteria were found almost exclusively on the adaxial surface while only a small group of leaf blotch fungi thrived on the abaxial surface. There are higher densities of stomata on the abaxial surface and these openings are vulnerable ports of entry into the plant host. While one might argue about the settling of dust particles and microorganisms on the adaxial surface, we detected differences in reactive chemical activities and microstructures between the adaxial and abaxial surfaces. Our results further suggest that both plant species deploy different defence strategies to deter invading pathogens on the abaxial surface. We hypothesize that chemical and mechanical defence strategies evolved independently for harnessing and controlling phyllosphere microbiomes. Our findings have also advanced our understanding that the abaxial leaf surface is distinct from the adaxial surface and that the reduced microbial diversity is likely a consequence of plant-microbe interactions.

Population-specific positive selection on low CR1 expression in malaria-endemic regions.

Complement Receptor Type 1 (CR1) is a malaria-associated gene that encodes a transmembrane receptor of erythrocytes and is crucial for malaria parasite invasion. The expression of CR1 contributes to the rosetting of erythrocytes in the brain bloodstream, causing cerebral malaria, the most severe form of the disease. Here, we study the history of adaptation against malaria by analyzing selection signals in the CR1 gene. We used whole-genome sequencing datasets of 907 healthy individuals from malaria-endemic and non-endemic populations. We detected robust positive selection in populations from the hyperendemic regions of East India and Papua New Guinea. Importantly, we identified a new adaptive variant, rs12034598, which is associated with a slower rate of erythrocyte sedimentation and is linked with a variant associated with low levels of CR1 expression. The combination of the variants likely drives natural selection. In addition, we identified a variant rs3886100 under positive selection in West Africans, which is also related to a low level of CR1 expression in the brain. Our study shows the fine-resolution history of positive selection in the CR1 gene and suggests a population-specific history of CR1 adaptation to malaria. Notably, our novel approach using population genomic analyses allows the identification of protective variants that reduce the risk of malaria infection without the need for patient samples or malaria individual medical records. Our findings contribute to understanding of human adaptation against cerebral malaria.

Vertical stratification of the air microbiome in the lower troposphere.

The troposphere constitutes the final frontier of global ecosystem research due to technical challenges arising from its size, low biomass, and gaseous state. Using a vertical testing array comprising a meteorological tower and a research aircraft, we conducted synchronized measurements of meteorological parameters and airborne biomass (n = 480) in the vertical air column up to 3,500 m. The taxonomic analysis of metagenomic data revealed differing patterns of airborne microbial community composition with respect to time of day and height above ground. The temporal and spatial resolution of our study demonstrated that the diel cycle of airborne microorganisms is a ground-based phenomenon that is entirely absent at heights >1,000 m. In an integrated analysis combining meteorological and biological data, we demonstrate that atmospheric turbulence, identified by potential temperature and high-frequency three-component wind measurements, is the key driver of bioaerosol dynamics in the lower troposphere. Multivariate regression analysis shows that at least 50% of identified airborne microbial taxa (n = ∼10,000) are associated with either ground or height, allowing for an understanding of dispersal patterns of microbial taxa in the vertical air column. Due to the interconnectedness of atmospheric turbulence and temperature, the dynamics of microbial dispersal are likely to be impacted by rising global temperatures, thereby also affecting ecosystems on the planetary surface.

Short-range contributions of local sources to ambient air.

Recent developments in aerobiology have enabled the investigation of airborne biomass with high temporal and taxonomic resolution. In this study, we assess the contributions of local sources to ambient air within a 160,000 m2 tropical avian park (AP). We sequenced and analyzed 120 air samples from seven locations situated 160 to 400 m apart, representing distinct microhabitats. Each microhabitat contained a characteristic air microbiome, defined by the abundance and richness of its airborne microbial community members, supported by both, PCoA and Random Forest analysis. Each outdoor microhabitat contained 1% to 18.6% location-specific taxa, while a core microbiome of 27.1% of the total taxa was shared. To identify and assess local sources, we compared the AP dataset with a DVE reference dataset from a location 2 km away, collected during a year-round sampling campaign. Intersection of data from the two sites demonstrated 61.6% of airborne species originated from local sources of the AP, 34.5% from ambient air background, and only 3.9% of species were specific to the DVE reference site. In-depth taxonomic analysis demonstrated association of bacteria-dominated air microbiomes with indoor spaces, while fungi-dominated airborne microbial biomass was predominant in outdoor settings with ample vegetation. The approach presented here demonstrates an ability to identify local source contributions against an ambient air background, despite the prevailing mixing of air masses caused by atmospheric turbulences.

Population-specific adaptation in malaria-endemic regions of asia.

Evolutionary mechanisms of adaptation to malaria are understudied in Asian endemic regions despite a high prevalence of malaria in the region. In our research, we performed a genome-wide screening for footprints of natural selection against malaria by comparing eight Asian population groups from malaria-endemic regions with two non-endemic population groups from Europe and Mongolia. We identified 285 adaptive genes showing robust selection signals across three statistical methods, iHS, XP-EHH, and PBS. Interestingly, most of the identified genes (82%) were found to be under selection in a single population group, while adaptive genes shared across populations were rare. This is likely due to the independent adaptation history in different endemic populations. The gene ontology (GO) analysis for the 285 adaptive genes highlighted their functional processes linked to neuronal organizations or nervous system development. These genes could be related to cerebral malaria and may reduce the inflammatory response and the severity of malaria symptoms. Remarkably, our novel population genomic approach identified population-specific adaptive genes potentially against malaria infection without the need for patient samples or individual medical records.

Experimental parameters defining ultra-low biomass bioaerosol analysis.

Investigation of the microbial ecology of terrestrial, aquatic and atmospheric ecosystems requires specific sampling and analytical technologies, owing to vastly different biomass densities typically encountered. In particular, the ultra-low biomass nature of air presents an inherent analytical challenge that is confounded by temporal fluctuations in community structure. Our ultra-low biomass pipeline advances the field of bioaerosol research by significantly reducing sampling times from days/weeks/months to minutes/hours, while maintaining the ability to perform species-level identification through direct metagenomic sequencing. The study further addresses all experimental factors contributing to analysis outcome, such as amassment, storage and extraction, as well as factors that impact on nucleic acid analysis. Quantity and quality of nucleic acid extracts from each optimisation step are evaluated using fluorometry, qPCR and sequencing. Both metagenomics and marker gene amplification-based (16S and ITS) sequencing are assessed with regard to their taxonomic resolution and inter-comparability. The pipeline is robust across a wide range of climatic settings, ranging from arctic to desert to tropical environments. Ultimately, the pipeline can be adapted to environmental settings, such as dust and surfaces, which also require ultra-low biomass analytics.

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure.

BACKGROUND: In genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework. METHODS: To identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on a model-based multifactor dimensionality reduction approach for structured populations, namely MBMDR-PC, MBMDR-PG, and MBMDR-GC. RESULTS: Simulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power. CONCLUSION: We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.

Taxonomic composition and seasonal dynamics of the air microbiome in West Siberia.

Here, we describe taxonomical composition, as well as seasonal and diel dynamics of airborne microbial communities in West Siberia. A total of 78 airborne biomass samples from 39 time intervals were analysed, within a temperature range of 48 °C (26 °C to - 22 °C). We observed a 5-170-fold decrease in DNA yield extracted from the airborne biomass in winter compared to summer, nevertheless, yielding sufficient material for metagenomic analysis. The airborne microbial communities included Actinobacteria and Proteobacteria, Ascomycota and Basidiomycota fungi as major components, as well as some Streptophyta plants. In summer, bacterial and fungal plant pathogens, and wood-rotting saprophytes were predominant. In winter, Ascomycota moulds and cold-related or stress environment bacterial species were enriched, while the fraction of wood-rotting and mushroom-forming Basidiomycota fungi was largely reduced. As recently reported for the tropical climate, the airborne microbial communities performed a diel cycle in summer, however, in winter diel dynamics were not observed.

Microbial communities in the tropical air ecosystem follow a precise diel cycle.

The atmosphere is vastly underexplored as a habitable ecosystem for microbial organisms. In this study, we investigated 795 time-resolved metagenomes from tropical air, generating 2.27 terabases of data. Despite only 9 to 17% of the generated sequence data currently being assignable to taxa, the air harbored a microbial diversity that rivals the complexity of other planetary ecosystems. The airborne microbial organisms followed a clear diel cycle, possibly driven by environmental factors. Interday taxonomic diversity exceeded day-to-day and month-to-month variation. Environmental time series revealed the existence of a large core of microbial taxa that remained invariable over 13 mo, thereby underlining the long-term robustness of the airborne community structure. Unlike terrestrial or aquatic environments, where prokaryotes are prevalent, the tropical airborne biomass was dominated by DNA from eukaryotic phyla. Specific fungal and bacterial species were strongly correlated with temperature, humidity, and CO2 concentration, making them suitable biomarkers for studying the bioaerosol dynamics of the atmosphere.

Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies.

BACKGROUND: In Genome-Wide Association Studies (GWAS), the concept of linkage disequilibrium is important as it allows identifying genetic markers that tag the actual causal variants. In Genome-Wide Association Interaction Studies (GWAIS), similar principles hold for pairs of causal variants. However, Linkage Disequilibrium (LD) may also interfere with the detection of genuine epistasis signals in that there may be complete confounding between Gametic Phase Disequilibrium (GPD) and interaction. GPD may involve unlinked genetic markers, even residing on different chromosomes. Often GPD is eliminated in GWAIS, via feature selection schemes or so-called pruning algorithms, to obtain unconfounded epistasis results. However, little is known about the optimal degree of GPD/LD-pruning that gives a balance between false positive control and sufficient power of epistasis detection statistics. Here, we focus on Model-Based Multifactor Dimensionality Reduction as one large-scale epistasis detection tool. Its performance has been thoroughly investigated in terms of false positive control and power, under a variety of scenarios involving different trait types and study designs, as well as error-free and noisy data, but never with respect to multicollinear SNPs. RESULTS: Using real-life human LD patterns from a homogeneous subpopulation of British ancestry, we investigated the impact of LD-pruning on the statistical sensitivity of MB-MDR. We considered three different non-fully penetrant epistasis models with varying effect sizes. There is a clear advantage in pre-analysis pruning using sliding windows at r 2 of 0.75 or lower, but using a threshold of 0.20 has a detrimental effect on the power to detect a functional interactive SNP pair (power < 25%). Signal sensitivity, directly using LD-block information to determine whether an epistasis signal is present or not, benefits from LD-pruning as well (average power across scenarios: 87%), but is largely hampered by functional loci residing at the boundaries of an LD-block. CONCLUSIONS: Our results confirm that LD patterns and the position of causal variants in LD blocks do have an impact on epistasis detection, and that pruning strategies and LD-blocks definitions combined need careful attention, if we wish to maximize the power of large-scale epistasis screenings.

Multiple Epistasis Interactions Within MHC Are Associated With Ulcerative Colitis.

Successful searching for epistasis is much challenging, which generally requires very large sample sizes and/or very dense marker information. We exploited the largest Crohn's disease (CD) dataset (18,000 cases + 34,000 controls) and ulcerative colitis (UC) dataset (14,000 cases + 34,000 controls) to date. Leveraging its dense marker information and the large sample size of this IBD dataset, we employed a two-step approach to exhaustively search for epistasis. We detected abundant genome-wide significant (p < 1 × 10-13) epistatic signals, all within the MHC region. These signals were reduced substantially when conditional on the additive background, but still nine pairs remained significant at the Immunochip-wide level (P < 1.1 × 10-8) in conditional tests for UC. All these nine epistatic interactions come from the MHC region, and each explains on average 0.15% of the phenotypic variance. Eight of them were replicated in a replication cohort. There are multiple but relatively weak interactions independent of the additive effects within the MHC region for UC. Our promising results warrant the search for epistasis in large data sets with dense markers, exploiting dependencies between markers.

Male-specific epistasis between WWC1 and TLN2 genes is associated with Alzheimer's disease.

Systematic epistasis analyses in multifactorial disorders are an important step to better characterize complex genetic risk structures. We conducted a hypothesis-free sex-stratified genome-wide screening for epistasis contributing to Alzheimer's disease (AD) susceptibility. We identified a statistical epistasis signal between the single nucleotide polymorphisms rs3733980 and rs7175766 that was associated with AD in males (genome-wide significant pBonferroni-corrected=0.0165). This signal pointed toward the genes WW and C2 domain containing 1, aka KIBRA; 5q34 and TLN2 (talin 2; 15q22.2). Gene-based meta-analysis in 3 independent consortium data sets confirmed the identified interaction: the most significant (pmeta-Bonferroni-corrected=9.02*10-3) was for the single nucleotide polymorphism pair rs1477307 and rs4077746. In functional studies, WW and C2 domain containing 1, aka KIBRA and TLN2 coexpressed in the temporal cortex brain tissue of AD subjects (ß=0.17, 95% CI 0.04 to 0.30, p=0.01); modulated Tau toxicity in Drosophila eye experiments; colocalized in brain tissue cells, N2a neuroblastoma, and HeLa cell lines; and coimmunoprecipitated both in brain tissue and HEK293 cells. Our finding points toward new AD-related pathways and provides clues toward novel medical targets for the cure of AD.

Complete Genome Sequence of Bacillus altitudinis Type Strain SGAir0031 Isolated from Tropical Air Collected in Singapore.

Bacillus altitudinis strain SGAir0031 (Firmicutes) was isolated from tropical air samples collected in Singapore. Its genome was assembled using short reads and single-molecule real-time sequencing, comprising one chromosome with 3.81 Mb and one plasmid with 32 kb. The genome consists of 3,820 protein-coding genes, 81 tRNAs, and 24 rRNAs.

A cautionary note on the impact of protocol changes for genome-wide association SNP × SNP interaction studies: an example on ankylosing spondylitis.

Genome-wide association interaction (GWAI) studies have increased in popularity. Yet to date, no standard protocol exists. In practice, any GWAI workflow involves making choices about quality control strategy, SNP filtering, linkage disequilibrium (LD) pruning, analytic tool to model or to test for genetic interactions. Each of these can have an impact on the final epistasis findings and may affect their reproducibility in follow-up analyses. Choosing an analytic tool is not straightforward, as different tools exist and current understanding about their performance is based on often very particular simulation settings. In the present study, we wish to create awareness for the impact of (minor) changes in a GWAI analysis protocol can have on final epistasis findings. In particular, we investigate the influence of marker selection and marker prioritization strategies, LD pruning and the choice of epistasis detection analytics on study results, giving rise to 8 GWAI protocols. Discussions are made in the context of the ankylosing spondylitis (AS) data obtained via the Wellcome Trust Case Control Consortium (WTCCC2). As expected, the largest impact on AS epistasis findings is caused by the choice of marker selection criterion, followed by marker coding and LD pruning. In MB-MDR, co-dominant coding of main effects is more robust to the effects of LD pruning than additive coding. We were able to reproduce previously reported epistasis involvement of HLA-B and ERAP1 in AS pathology. In addition, our results suggest involvement of MAGI3 and PARK2, responsible for cell adhesion and cellular trafficking. Gene ontology biological function enrichment analysis across the 8 considered GWAI protocols also suggested that AS could be associated to the central nervous system malfunctions, specifically, in nerve impulse propagation and in neurotransmitters metabolic processes.

High-density mapping of the MHC identifies a shared role for HLA-DRB1*01:03 in inflammatory bowel diseases and heterozygous advantage in ulcerative colitis.

Genome-wide association studies of the related chronic inflammatory bowel diseases (IBD) known as Crohn's disease and ulcerative colitis have shown strong evidence of association to the major histocompatibility complex (MHC). This region encodes a large number of immunological candidates, including the antigen-presenting classical human leukocyte antigen (HLA) molecules. Studies in IBD have indicated that multiple independent associations exist at HLA and non-HLA genes, but they have lacked the statistical power to define the architecture of association and causal alleles. To address this, we performed high-density SNP typing of the MHC in >32,000 individuals with IBD, implicating multiple HLA alleles, with a primary role for HLA-DRB1*01:03 in both Crohn's disease and ulcerative colitis. Noteworthy differences were observed between these diseases, including a predominant role for class II HLA variants and heterozygous advantage observed in ulcerative colitis, suggesting an important role of the adaptive immune response in the colonic environment in the pathogenesis of IBD.

Practical aspects of genome-wide association interaction analysis.

Large-scale epistasis studies can give new clues to system-level genetic mechanisms and a better understanding of the underlying biology of human complex disease traits. Though many novel methods have been proposed to carry out such studies, so far only a few of them have demonstrated replicable results. Here, we propose a minimal protocol for genome-wide association interaction (GWAI) analysis to identify gene-gene interactions from large-scale genomic data. The different steps of the developed protocol are discussed and motivated, and encompass interaction screening in a hypothesis-free and hypothesis-driven manner. In particular, we examine a wide range of aspects related to epistasis discovery in the context of complex traits in humans, hereby giving practical recommendations for data quality control, variant selection or prioritization strategies and analytic tools, replication and meta-analysis, biological validation of statistical findings and other related aspects. The minimal protocol provides guidelines and attention points for anyone involved in GWAI analysis and aims to enhance the biological relevance of GWAI findings. At the same time, the protocol improves a better assessment of strengths and weaknesses of published GWAI methodologies.